Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Decision Fusion
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
Detection of mines and minelike targets using principal component and neural-network methods
IEEE Transactions on Neural Networks
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Feature combination using boosting
Pattern Recognition Letters
Computer vision techniques for forest fire perception
Image and Vision Computing
Estimation and decision fusion: A survey
Neurocomputing
Concept-based evidential reasoning for multimodal fusion in human-computer interaction
Applied Soft Computing
Comparison of adaboost and genetic programming for combining neural networks for drug discovery
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Landmines recognition system using thermovision techniques
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A hybrid data-fusion system using modal data and probabilistic neural network for damage detection
Advances in Engineering Software
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We present and compare methods for feature-level (predetection) and decision-level (postdetection) fusion of multisensor data. This study emphasizes fusion techniques that are suitable for noncommensurate data sampled at noncoincident points. Decision-level fusion is most convenient for such data, but it is suboptimal in principle, since targets not detected by all sensors will not obtain the full benefits of fusion. A novel algorithm for feature-level fusion of noncommensurate, noncoincidently sampled data is described, in which a model is fitted to the sensor data and the model parameters are used as features. Formulations for both feature-level and decision-level fusion are described, along with some practical simplifications. A closed-form expression is available for feature-level fusion of normally distributed data and this expression is used with simulated data to study requirements for sample position accuracy in multisensor data. The performance of feature-level and decision-level fusion algorithms are compared for experimental data acquired by a metal detector, a ground-penetrating radar, and an infrared camera at a challenging test site containing surrogate mines. It is found that fusion of binary decisions does not perform significantly better than the best available sensor. The performance of feature-level fusion is significantly better than the individual sensors, as is decision-level fusion when detection confidence information is also available (驴soft-decision驴 fusion).